# Goal

Discuss machine learning techniques.

# Schedule

Date | Presenter | Paper | Comments |
---|---|---|---|

1: 27-10-2015 | Alexander Ly | Breiman, L (2001) and Ch 2 | Slides |

2: 17-11-2015 | Tahira Jamil | Linear regression (Ch 3) | Slides |

3: 01-12-2015 | Lourens Waldorp | Logistic Regression | Slides |

4: 12-01-2016 | Johnny van Doorn, Quentin Gronau | Resampling techniques | Slides a, Slides b |

5: 26-01-2016 | Lourens Waldorp | Regularisation: Ridge, lasso | Slides |

6: 09-02-2016 | Lourens Waldorp | Gaussian graphical models and the graphical lasso | Slides |

7: 23-02-2016 | Alexander Ly | Motivating splines | Slides |

8: 08-03-2016 | Alexander Ly | Smoothing splines | Slides |

9: 29-03-2016 | Riet van Bork | Classification and regression trees | Slides |

10: 05-04-2016 | Joost Kruis | Neural networks | Slides |

11: 19-04-2016 | Jonas Haslbeck | Bagging | Slides |

12: 10-05-2016 | Gilles de Hollander | Machine learning in cognitive neuroscience | Slides |

13: 31-05-2016 | Udo Bohm | Support vector machines | Slides |

14: 02-11-2016 | Alexander Ly | Recap | Slides |

15: 23-11-2016 | Jonas Haslbeck | Clustering | Slides |

16: 07-12-2016 | Pia Tio | Dimensionality reduction | Slides |

17: 25-01-2017 | Gilles de Hollander | Mixture modelling | Slides |

18: 15-02-2017 | Don van den Bergh | Recommender systems | Slides |

19: 01-03-2017 | Johnny van Doorn | PageRank | |

20: 22-03-2017 | Koen Derks | N-grams | |

21: 12-04-2017 | Claire Stevenson | Topic modelling | |

20: 26-04-2017 | Quentin Gronau | Topic modelling and Bayesian nonparametrics |

# Literature

– James, G, Witten, D, Hastie, T, Tibshirani, R (2013). An introduction to statistical learning – with applications in R

– Hastie, T, Tibshirani, R, Friedman, JH (2001), The elements of statistical learning Data mining, inference, and prediction

– Breiman, L (2001). Statistical modeling The two cultures

## Suggested reading

#### 1: Alexander Ly – General overview

– Breiman, L (2001). Statistical modeling The two cultures”

– James, G, Witten, D, Hastie, T, Tibshirani, R (2013). An introduction to statistical learning – with applications in R – Ch 1, 2

– Hastie, T, Tibshirani, R, Friedman, JH (2001), The elements of statistical learning Data mining, inference, and prediction – Ch1

#### 2: Tahira Jamil – Linear regression

– James, G, Witten, D, Hastie, T, Tibshirani, R (2013). An introduction to statistical learning – with applications in R – Ch 3

– Hastie, T, Tibshirani, R, Friedman, JH (2001), The elements of statistical learning Data mining, inference, and prediction –

#### 3: Lourens Waldorp – Logistic regression

– James, G, Witten, D, Hastie, T, Tibshirani, R (2013). An introduction to statistical learning – with applications in R – Ch 4

– Hastie, T, Tibshirani, R, Friedman, JH (2001), The elements of statistical learning Data mining, inference, and prediction –

#### 4: Johnny van Doorn – K-fold cross validation and Quentin Gronau – Bootstrapping

– James, G, Witten, D, Hastie, T, Tibshirani, R (2013). An introduction to statistical learning – with applications in R – Ch 5

#### 5: Lourens Waldorp – Regularisation: Ridge, lasso

– James, G, Witten, D, Hastie, T, Tibshirani, R (2013). An introduction to statistical learning – with applications in R – Ch 6

#### 6: Lourens Waldorp – Gaussian graphical models and the graphical lasso

#### 7: Alexander Ly – Motivating: Splines

– James, G, Witten, D, Hastie, T, Tibshirani, R (2013). An introduction to statistical learning – with applications in R – Ch 7

– Hastie, T, Tibshirani, R, Friedman, JH (2001), The elements of statistical learning Data mining, inference, and prediction – Ch 5

#### 9: Riet van Bork – Classification and regression trees

– James, G, Witten, D, Hastie, T, Tibshirani, R (2013). An introduction to statistical learning – with applications in R – Ch 8

– Hastie, T, Tibshirani, R, Friedman, JH (2001), The elements of statistical learning Data mining, inference, and prediction – Ch 9

#### 10: Joost Kruis – Neural networks

– Hastie, T, Tibshirani, R, Friedman, JH (2001), The elements of statistical learning Data mining, inference, and prediction – Ch 11

#### Jonas Haslbeck – Bagging

– James, G, Witten, D, Hastie, T, Tibshirani, R (2013). An introduction to statistical learning – with applications in R – Ch 8.2

#### 13: Udo Bohm- Suppor vector machines

– James, G, Witten, D, Hastie, T, Tibshirani, R (2013). An introduction to statistical learning – with applications in R – Ch 9

– Hastie, T, Tibshirani, R, Friedman, JH (2001), The elements of statistical learning Data mining, inference, and prediction – Ch 12

#### 15: Jonas Haslbeck – Clustering

– James, G, Witten, D, Hastie, T, Tibshirani, R (2013). An introduction to statistical learning – with applications in R – Ch 10

– Hastie, T, Tibshirani, R, Friedman, JH (2001), The elements of statistical learning Data mining, inference, and prediction – Ch 14

#### 16: Pia Tio – Dimensionality reduction

– James, G, Witten, D, Hastie, T, Tibshirani, R (2013). An introduction to statistical learning – with applications in R – Ch 10

#### 17: Gilles de Hollander – Mixture modelling

– Hastie, T, Tibshirani, R, Friedman, JH (2001), The elements of statistical learning Data mining, inference, and prediction – Section 6.8, 8.5, 13.2.3, 14.3.7,

– Marin, JM, Robert, CP (2014). Bayesian essentials with R – Chapter 6

– Bishop, CM (2006). Pattern recognition and machine learning – Section 2.3.9, 5.6, Chapter 9

– Murphy, KP (2012). Machine learning – A probabilistic perspective – Chapter 11

#### 18: Don van den Bergh – Recommender systems

– Ricci, F, Rokach, L, Shapira, B, Kantor, PB (2010). Recommender Systems Handbook – Ch1

– Gorakala, SK, Usuelli, M (2015). Building a Recommendation System with R- Learn the art of building robust and powerful recommendation engines using R

– https://www.coursera.org/learn/recommender-systems-introduction

#### 19: Johnny van Doorn – PageRank

– Barber, D (2015). Bayesian reasoning and machine learning – Ch 23

– Murphy, KP (2012). Machine learning – A probabilistic perspective – Ch 17

– http://www.ams.org/samplings/feature-column/fcarc-pagerank

– Russell, S, Norvig, P (2009). Artificial intelligence – A modern approach – 3rd – Ch 22.3

#### 20: Koen Derks – Text mining

– https://www.youtube.com/watch?t=25s&v=GTrkTDCyO80&app=desktop

#### 20: Claire Stevenson – Topic modelling

–

#### 20: Quentin Gronau – Topic modelling and Bayesian nonparametrics

–

# Videos

– Videos by Trevor Hastie and Rob Tibshirani

– MIT Opencourseware: Artificial Intelligence Videos by Patrick Henry Winston